From 7dc76c6ff6b51749f39f3fabecee27417a77b1df Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Fri, 16 Mar 2018 12:39:45 +0100
Subject: [PATCH] Add test for textcat

---
 spacy/tests/test_textcat.py | 41 +++++++++++++++++++++++++++++++++++++
 1 file changed, 41 insertions(+)
 create mode 100644 spacy/tests/test_textcat.py

diff --git a/spacy/tests/test_textcat.py b/spacy/tests/test_textcat.py
new file mode 100644
index 000000000..20f21131a
--- /dev/null
+++ b/spacy/tests/test_textcat.py
@@ -0,0 +1,41 @@
+import random
+
+from ..pipeline import TextCategorizer
+from ..lang.en import English
+from ..vocab import Vocab
+from ..tokens import Doc
+from ..gold import GoldParse
+
+
+def test_textcat_learns_multilabel():
+    docs = []
+    nlp = English()
+    vocab = nlp.vocab
+    letters = ['a', 'b', 'c']
+    for w1 in letters:
+        for w2 in letters:
+            cats = {letter: float(w2==letter) for letter in letters}
+            docs.append((Doc(vocab, words=['d']*3 + [w1, w2] + ['d']*3), cats))
+    random.shuffle(docs)
+    model = TextCategorizer(vocab, width=8)
+    for letter in letters:
+        model.add_label(letter)
+    optimizer = model.begin_training()
+    for i in range(20):
+        losses = {}
+        Ys = [GoldParse(doc, cats=cats) for doc, cats in docs]
+        Xs = [doc for doc, cats in docs]
+        model.update(Xs, Ys, sgd=optimizer, losses=losses)
+        random.shuffle(docs)
+    for w1 in letters:
+        for w2 in letters:
+            doc = Doc(vocab, words=['d']*3 + [w1, w2] + ['d']*3)
+            truth = {letter: w2==letter for letter in letters}
+            model(doc)
+            for cat, score in doc.cats.items():
+                print(doc, cat, score)
+                if not truth[cat]:
+                    assert score < 0.5
+                else:
+                    assert score > 0.5
+